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ABOUT

I'm an undergraduate student at the California Institute of Technology who studies quantum information, high energy physics, and the intersection of machine learning and science.

RESEARCH

  • I'm currently developing error mitigation methods for quantum circuits at the Institute of Quantum Information and Matter at Caltech, using machine learning to create an intelligent compiler for noisy intermediate-scale quantum devices.
  • I proposed and developed energy correction and clustering components in an end-to-end deep learning pipeline for neutrino experiments (DUNE and MicroBooNE) with the DeepLearnPhysics collaboration at Stanford, using semantic segmentation and graph neural networks.
  • I demonstrated how deep learning can find particle tracks in the Large Hadron Collider at CERN under the HEP.Trkx project using graph neural networks.
  • I proposed a new quantum machine learning algorithm for Higgs boson classification on the D-Wave quantum computer, as well as quantum annealing for particle tracking at the Large Hadron Collider.
  • I simulated black hole collisions and implemented Bayesian methods for detecting the astrophysical gravitational wave background at LIGO.
  • I developed genetic algorithm optimization for neutron imaging analysis of scintillating crystals at the Berkeley Space Sciences Lab, publishing our results in a peer-reviewed paper.

INVITED TALKS

  • "Novel machine learning algorithms for quantum annealing with applications in high energy physics.” Quantum Techniques in Machine Learning, Korea Advanced Institute of Science and Technology (KAIST), October 2019.
  • "Machine learning applications of quantum annealing in high energy physics.” AI-at-SLAC Seminar, Stanford Linear Accelerator Center, August 2019. (Abstract here.)

PAPERS & PROCEEDINGS

  • A. Zlokapa, A. Mott, J. Job, J.-R. Vlimant, D. Lidar and M. Spiropulu, “Quantum adiabatic machine learning with zooming.” arXiv:1908.04480 [quant-ph], 2019. (To be published. Available here.)
  • A. Zlokapa, A. Anand, J.-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu, “Charged particle tracking with quantum annealing-inspired optimization.” arXiv:1908.04475 [quant-ph], 2019. (To be published. Available here.)
  • J.-R. Vlimant, F. Pantaleo, M. Pierini, V. Loncar, S. Vallecorsa, D. Anderson, T. Nguyen, and A. Zlokapa, "Training Generative Adversarial Models over Distributed Computing Systems.” Proceedings of the 23rd International Conference on Computing in High Energy and Nuclear Physics, 2018. (Accepted.)
  • A. Tremsin, D. Perrodin, A. Losko, S. Vogel, T. Shinohara, K. Oikawa, J. Peterson, C. Zhang, J. Derby, A. Zlokapa, G. Bizarii and E. Bourret, "In-Situ Observation of Phase Separation During Growth of Cs2LiLaBr6:Ce Crystals Using Energy-Resolved Neutron Imaging." Crystal Growth & Design, 2017, 17 (12), 6372-6381. https://doi.org/10.1021/acs.cgd.7b01048

CONFERENCES

  • X. Ju, A. Zlokapa, S. Farrell, J.-R. Vlimant, L. Gray, P. Calafiura, and M. Spiropulu, “Graph Neural Networks for Particle Reconstruction in High Energy Physics Detectors.” 33rd Annual Conference on Neural Information Processing Systems, Machine Learning for Physical Sciences Workshops, December 2019.
  • A. Zlokapa and A. Gheorghiu, “A deep learning approach to noise prediction and circuit optimization for near-term quantum devices.” IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, November 2019.
  • A. Zlokapa et al., “Novel machine learning algorithms for quantum annealing with applications in high energy physics.” Qubits North America, September 2019.
  • X. Ju, A. Zlokapa, A. Anand, J.-R. Vlimant, S. Farrell, P. Calafiura, and M. Spiropulu, “HEP.TrkX Charged Particle Tracking Using Graph Neural Networks.” Connecting the Dots / Intelligent Trackers Workshop, April 2019. (Available here. Different talk from ACAT below.)
  • A. Zlokapa, A. Anand, J.-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu, “Charged Particle Tracking as a QUBO Problem Solved with Quantum Annealing-Inspired Optimization.” Connecting the Dots / Intelligent Trackers Workshop, April 2019. (Available here. Same talk as at ACAT below.)
  • X. Ju, A. Zlokapa, A. Anand, J.-R. Vlimant, S. Farrell, P. Calafiura, and M. Spiropulu, “HEP.TrkX Charged Particle Tracking Using Graph Neural Networks.” 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2019. (Available here.)
  • A. Zlokapa, A. Anand, J.-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu, “Charged Particle Tracking as a QUBO Problem Solved with Quantum Annealing-Inspired Optimization.” 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, 2019. (Available here.)
  • A. Zlokapa, J.-R. Vlimant, and M. Spiropulu, "Optimizing Monte Carlo Event Generation Using Evolutionary Computing Techniques." CMS Week (Budapest), 2018. (Available here.)
  • J.-R. Vlimant, F. Pantaleo, M. Pierini, V. Loncar, S. Vallecorsa, D. Anderson, T. Nguyen, and A. Zlokapa, "Training Generative Adversarial Models over Distributed Computing Systems." 23rd International Conference on Computing in High Energy and Nuclear Physics, 2018. (Available here.)

ACHIEVEMENTS

My CV contains the full list, but here are some highlights:

  • 1st place, Citadel Data Open International Championship (and 1st in West Coast Regional)
  • Hacktech (MLH@Caltech): Best Machine Learning Hack, Best Hardware Hack, Best IoT Hack
  • 2nd place, Intel International Science & Engineering Fair
  • Perfect SAT Score (top 0.02% nationally)
  • National Merit Scholar
  • US Presidential Scholar Candidate
  • Minor Planet 34134 Zlokapa (MIT Lincoln Lab)
  • American Invitational Mathematics Exam qualifier
  • Study of Exceptional Talent, Johns Hopkins' Center for Talented Youth

PORTFOLIO

I founded and am president of the Caltech Data Science Organization, where I enjoy working on projects in my free time, including machine learning tools to fight malaria, a virtual reality prototype for safer AI-powered firearms, supersonic rocket simulations for the FAR-MARS Prize, and deep learning for music composition.